---
title: "More with LESS"
#author: "Machiel Visser"
#date: December 1, 2019
output:
flexdashboard::flex_dashboard:
orientation: columns #rows
vertical_layout: scroll #fill
social: menu
source_code: embed
---
```{r Load objects, include=FALSE}
load("FinalPlots_Simulations.RData")
load("FinalPlots_Data.RData")
load("FinalPlots_Data_SampleSize.RData")
```
# Simulation 1
## Column
### **Model dimensionality** when noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.
```{r sim1_var01_numbetas}
plot_sim1_var01_numbetas
```
### **Model dimensionality** when noise $\sim N(\mu = 0, \sigma^2 = 1)$.
```{r sim1_var1_numbetas}
plot_sim1_var1_numbetas
```
### **Model dimensionality** when noise $\sim N(\mu = 0, \sigma^2 = 10)$.
```{r sim1_var10_numbetas}
plot_sim1_var10_numbetas
```
## Column
### **AUC** when noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.
```{r sim1_var01_auc}
plot_sim1_var01_auc
```
### **AUC** when noise $\sim N(\mu = 0, \sigma^2 = 1)$.
```{r sim1_var1_auc}
plot_sim1_var1_auc
```
### **AUC** when noise $\sim N(\mu = 0, \sigma^2 = 10)$.
```{r sim1_var10_auc}
plot_sim1_var10_auc
```
# Simulation 2
## Column
### **Model dimensionality** when $P=2$.
```{r sim2_p2_numbetas}
plot_sim2_p2_numbetas
```
### **Model dimensionality** when $P=10$.
```{r sim2_p10_numbetas}
plot_sim2_p10_numbetas
```
### **Model dimensionality** when $P=100$.
```{r sim2_p100_numbetas}
plot_sim2_p100_numbetas
```
### **Model dimensionality** when $P=1000$.
```{r sim2_p1000_numbetas}
plot_sim2_p1000_numbetas
```
## Column
### **AUC** when $P=2$.
```{r sim2_p2_auc}
plot_sim2_p2_auc
```
### **AUC** when $P=10$.
```{r sim2_p10_auc}
plot_sim2_p10_auc
```
### **AUC** when $P=100$.
```{r sim2_p100_auc}
plot_sim2_p100_auc
```
### **AUC** when $P=1000$.
```{r sim2_p1000_auc}
plot_sim2_p1000_auc
```
# Simulation 3
## Column
### **Model dimensionality** when $P=2$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.
```{r sim3_p2_var01_numbetas}
plot_sim3_p2_var01_numbetas
```
### **Model dimensionality** when $P=2$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.
```{r sim3_p2_var1_numbetas}
plot_sim3_p2_var1_numbetas
```
### **Model dimensionality** when $P=2$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.
```{r sim3_p2_var10_numbetas}
plot_sim3_p2_var10_numbetas
```
### **Model dimensionality** when $P=10$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.
```{r sim3_p10_var01_numbetas}
plot_sim3_p10_var01_numbetas
```
### **Model dimensionality** when $P=10$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.
```{r sim3_p10_var1_numbetas}
plot_sim3_p10_var1_numbetas
```
### **Model dimensionality** when $P=10$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.
```{r sim3_p10_var10_numbetas}
plot_sim3_p10_var10_numbetas
```
### **Model dimensionality** when $P=100$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.
```{r sim3_p100_var01_numbetas}
plot_sim3_p100_var01_numbetas
```
### **Model dimensionality** when $P=100$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.
```{r sim3_p100_var1_numbetas}
plot_sim3_p100_var1_numbetas
```
### **Model dimensionality** when $P=100$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.
```{r sim3_p100_var10_numbetas}
plot_sim3_p100_var10_numbetas
```
### **Model dimensionality** when $P=1000$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.
```{r sim3_p1000_var01_numbetas}
plot_sim3_p1000_var01_numbetas
```
### **Model dimensionality** when $P=1000$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.
```{r sim3_p1000_var1_numbetas}
plot_sim3_p1000_var1_numbetas
```
### **Model dimensionality** when $P=1000$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.
```{r sim3_p1000_var10_numbetas}
plot_sim3_p1000_var10_numbetas
```
## Column
### **AUC** when $P=2$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.
```{r sim3_p2_var01_auc}
plot_sim3_p2_var01_auc
```
### **AUC** when $P=2$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.
```{r sim3_p2_var1_auc}
plot_sim3_p2_var1_auc
```
### **AUC** when $P=2$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.
```{r sim3_p2_var10_auc}
plot_sim3_p2_var10_auc
```
### **AUC** when $P=10$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.
```{r sim3_p10_var01_auc}
plot_sim3_p10_var01_auc
```
### **AUC** when $P=10$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.
```{r sim3_p10_var1_auc}
plot_sim3_p10_var1_auc
```
### **AUC** when $P=10$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.
```{r sim3_p10_var10_auc}
plot_sim3_p10_var10_auc
```
### **AUC** when $P=100$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.
```{r sim3_p100_var01_auc}
plot_sim3_p100_var01_auc
```
### **AUC** when $P=100$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.
```{r sim3_p100_var1_auc}
plot_sim3_p100_var1_auc
```
### **AUC** when $P=100$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.
```{r sim3_p100_var10_auc}
plot_sim3_p100_var10_auc
```
### **AUC** when $P=1000$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.
```{r sim3_p1000_var01_auc}
plot_sim3_p1000_var01_auc
```
### **AUC** when $P=1000$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.
```{r sim3_p1000_var1_auc}
plot_sim3_p1000_var1_auc
```
### **AUC** when $P=1000$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.
```{r sim3_p1000_var10_auc}
plot_sim3_p1000_var10_auc
```
# Simulation 4
## Column
### **Model dimensionality** when $P=2$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.
```{r sim4_p2_var01_numbetas}
plot_sim4_p2_var01_numbetas
```
### **Model dimensionality** when $P=2$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.
```{r sim4_p2_var1_numbetas}
plot_sim4_p2_var1_numbetas
```
### **Model dimensionality** when $P=2$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.
```{r sim4_p2_var10_numbetas}
plot_sim4_p2_var10_numbetas
```
### **Model dimensionality** when $P=10$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.
```{r sim4_p10_var01_numbetas}
plot_sim4_p10_var01_numbetas
```
### **Model dimensionality** when $P=10$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.
```{r sim4_p10_var1_numbetas}
plot_sim4_p10_var1_numbetas
```
### **Model dimensionality** when $P=10$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.
```{r sim4_p10_var10_numbetas}
plot_sim4_p10_var10_numbetas
```
### **Model dimensionality** when $P=100$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.
```{r sim4_p100_var01_numbetas}
plot_sim4_p100_var01_numbetas
```
### **Model dimensionality** when $P=100$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.
```{r sim4_p100_var1_numbetas}
plot_sim4_p100_var1_numbetas
```
### **Model dimensionality** when $P=100$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.
```{r sim4_p100_var10_numbetas}
plot_sim4_p100_var10_numbetas
```
### **Model dimensionality** when $P=1000$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.
```{r sim4_p1000_var01_numbetas}
plot_sim4_p1000_var01_numbetas
```
### **Model dimensionality** when $P=1000$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.
```{r sim4_p1000_var1_numbetas}
plot_sim4_p1000_var1_numbetas
```
### **Model dimensionality** when $P=1000$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.
```{r sim4_p1000_var10_numbetas}
plot_sim4_p1000_var10_numbetas
```
## Column
### **AUC** when $P=2$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.
```{r sim4_p2_var01_auc}
plot_sim4_p2_var01_auc
```
### **AUC** when $P=2$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.
```{r sim4_p2_var1_auc}
plot_sim4_p2_var1_auc
```
### **AUC** when $P=2$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.
```{r sim4_p2_var10_auc}
plot_sim4_p2_var10_auc
```
### **AUC** when $P=10$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.
```{r sim4_p10_var01_auc}
plot_sim4_p10_var01_auc
```
### **AUC** when $P=10$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.
```{r sim4_p10_var1_auc}
plot_sim4_p10_var1_auc
```
### **AUC** when $P=10$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.
```{r sim4_p10_var10_auc}
plot_sim4_p10_var10_auc
```
### **AUC** when $P=100$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.
```{r sim4_p100_var01_auc}
plot_sim4_p100_var01_auc
```
### **AUC** when $P=100$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.
```{r sim4_p100_var1_auc}
plot_sim4_p100_var1_auc
```
### **AUC** when $P=100$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.
```{r sim4_p100_var10_auc}
plot_sim4_p100_var10_auc
```
### **AUC** when $P=1000$ and noise $\sim N(\mu = 0, \sigma^2 = 0.1)$.
```{r sim4_p1000_var01_auc}
plot_sim4_p1000_var01_auc
```
### **AUC** when $P=1000$ and noise $\sim N(\mu = 0, \sigma^2 = 1)$.
```{r sim4_p1000_var1_auc}
plot_sim4_p1000_var1_auc
```
### **AUC** when $P=1000$ and noise $\sim N(\mu = 0, \sigma^2 = 10)$.
```{r sim4_p1000_var10_auc}
plot_sim4_p1000_var10_auc
```
# Simulation 5
## Column
### **Model dimensionality** when noise $\sim t(df = 1)$.
```{r sim5_numbetas}
plot_sim5_numbetas
```
## Column
### **AUC** when noise $\sim t(df = 1)$.
```{r sim5_auc}
plot_sim5_auc
```
# Real data
## Column
### **Model dimensionality** for *Colon* dataset.
```{r colon_numbetas}
plot_colon_numbetas
```
### **Model dimensionality** for *Glioma* dataset.
```{r glioma_numbetas}
plot_glioma_numbetas
```
### **Model dimensionality** for *Leukaemia* dataset.
```{r leukaemia_numbetas}
plot_leukaemia_numbetas
```
### **Model dimensionality** for *Lung* dataset.
```{r lung_numbetas}
plot_lung_numbetas
```
### **Model dimensionality** for *Metastasis* dataset.
```{r metas_numbetas}
plot_metas_numbetas
```
### **Model dimensionality** for *MLL* dataset.
```{r mll_numbetas}
plot_mll_numbetas
```
### **Model dimensionality** for *SRBCT* dataset.
```{r srbct_numbetas}
plot_srbct_numbetas
```
### **Model dimensionality** for *Wine* dataset.
```{r wine_numbetas}
plot_wine_numbetas
```
## Column
### **AUC** for *Colon* dataset.
```{r colon_auc}
plot_colon_auc
```
### **AUC** for *Glioma* dataset.
```{r glioma_auc}
plot_glioma_auc
```
### **AUC** for *Leukaemia* dataset.
```{r leukaemia_auc}
plot_leukaemia_auc
```
### **AUC** for *Lung* dataset.
```{r lung_auc}
plot_lung_auc
```
### **AUC** for *Metastasis* dataset.
```{r metas_auc}
plot_metas_auc
```
### **AUC** for *MLL* dataset.
```{r mll_auc}
plot_mll_auc
```
### **AUC** for *SRBCT* dataset.
```{r srbct_auc}
plot_srbct_auc
```
### **AUC** for *Wine* dataset.
```{r wine_auc}
plot_wine_auc
```
## Column
### **Model dimensionality** and **AUC** for *Colon* dataset.
```{r colon_2d}
plot_colon_2d
```
### **Model dimensionality** and **AUC** for *Glioma* dataset.
```{r glioma_2d}
plot_glioma_2d
```
### **Model dimensionality** and **AUC** for *Leukaemia* dataset.
```{r leukaemia_2d}
plot_leukaemia_2d
```
### **Model dimensionality** and **AUC** for *Lung* dataset.
```{r lung_2d}
plot_lung_2d
```
### **Model dimensionality** and **AUC** for *Metastasis* dataset.
```{r metas_2d}
plot_metas_2d
```
### **Model dimensionality** and **AUC** for *MLL* dataset.
```{r mll_2d}
plot_mll_2d
```
### **Model dimensionality** and **AUC** for *SRBCT* dataset.
```{r srbct_2d}
plot_srbct_2d
```
### **Model dimensionality** and **AUC** for *Wine* dataset.
```{r wine_2d}
plot_wine_2d
```
# Real data - Sample size
## Column
### **Model dimensionality** for *Colon* dataset.
```{r colon_samplesize_numbetas}
plot_colon_samplesize_numbetas
```
### **Model dimensionality** for *Glioma* dataset.
```{r glioma_samplesize_numbetas}
plot_glioma_samplesize_numbetas
```
### **Model dimensionality** for *Leukaemia* dataset.
```{r leukaemia_samplesize_numbetas}
plot_leukaemia_samplesize_numbetas
```
### **Model dimensionality** for *Lung* dataset.
```{r lung_samplesize_numbetas}
plot_lung_samplesize_numbetas
```
### **Model dimensionality** for *Metastasis* dataset.
```{r metas_samplesize_numbetas}
plot_metas_samplesize_numbetas
```
### **Model dimensionality** for *MLL* dataset.
```{r mll_samplesize_numbetas}
plot_mll_samplesize_numbetas
```
### **Model dimensionality** for *SRBCT* dataset.
```{r srbct_samplesize_numbetas}
plot_srbct_samplesize_numbetas
```
### **Model dimensionality** for *Wine* dataset.
```{r wine_samplesize_numbetas}
plot_wine_samplesize_numbetas
```
## Column
### **AUC** for *Colon* dataset.
```{r colon_samplesize_auc}
plot_colon_samplesize_auc
```
### **AUC** for *Glioma* dataset.
```{r glioma_samplesize_auc}
plot_glioma_samplesize_auc
```
### **AUC** for *Leukaemia* dataset.
```{r leukaemia_samplesize_auc}
plot_leukaemia_samplesize_auc
```
### **AUC** for *Lung* dataset.
```{r lung_samplesize_auc}
plot_lung_samplesize_auc
```
### **AUC** for *Metastasis* dataset.
```{r metas_samplesize_auc}
plot_metas_samplesize_auc
```
### **AUC** for *MLL* dataset.
```{r mll_samplesize_auc}
plot_mll_samplesize_auc
```
### **AUC** for *SRBCT* dataset.
```{r srbct_samplesize_auc}
plot_srbct_samplesize_auc
```
### **AUC** for *Wine* dataset.
```{r wine_samplesize_auc}
plot_wine_samplesize_auc
```
